Adaptive Processing of Technological Time Series for Forecasting Based on Neuro-Fuzzy Networks

Jumanov Isroil  Ibragimovich; Melieva Mokhinur  Baxromovna

doi:10.31149/ijhcs.v4i2.2721

Authors

Jumanov Isroil Ibragimovich
Ibragimovich@gmail.co
Doctor of Technical Sciences, Professor, Department of Information Technologies, Samarkand State University, Samarkand, Uzbekistan
Melieva Mokhinur Baxromovna Graduate student, Department of Information Technologies, Samarkand State University, Samarkand, Uzbekistan

Vol. 4 No. 2 (2022): IJHCS

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February 16, 2022

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Methodological bases for identification, data processing for forecasting technological time series based on the synthesis of soft computing apparatus (dynamic models, neural networks, neuro-fuzzy networks, genetic algorithms) in various combinations have been developed. A generalized prediction optimization algorithm based on a hybrid model with mechanisms for determining and adjusting the weights of neurons, coefficients of synaptic connections, activation functions, determining the number of layers and neurons in the layers of neural networks with a rational architecture is proposed.

Ibragimovich, J. I. ., & Baxromovna, M. M. . (2022). Adaptive Processing of Technological Time Series for Forecasting Based on Neuro-Fuzzy Networks. International Journal on Human-Computing Studies, 4(2), 30–35. https://doi.org/10.31149/ijhcs.v4i2.2721

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